Bibliographic record
Abstract
AbstractBased on research studies over the last 20 years with different satellite synthetic aperture radar (SAR) sensors, a short review of elevation modelling, digital terrain model (DTM) generation, and three-dimensional (3D) cartographic feature extraction using stereoscopic and polarimetric methods is given. The results of these research studies were used to evaluate the potential of RADARSAT-2 and three of its new characteristics for mapping applications: ultra-fine mode, better orbit knowledge, and polarimetry. Stereoscopy and polarimetry can be used to improve the DTM generation when compared with RADARSAT-1. In the best case, 5-m accuracy (68% confidence level) can be expected in moderate topography. Three-dimensional feature extraction using stereoscopic ultra-fine mode data can meet the National Topographic Database standard (better than 10-m positioning, 90% confidence level). Polarimetry with two images from crossing orbits (quasi-orthogonal in the north) can also be used for DTM generation depending on the topographic and land-cover conditions. The major drawback is the complex scattering models over forest or agricultural lands with C-band SAR data. In short term, the method can be applied in bare surfaces. All these forecast improvements should be confirmed with real data. A partir des recherches sur des capteurs radar à synthèse d'ouverture (RSO) de satellites obtenues ces vingt dernières années, un état de l'art sur la modélisation de l'altitude, la création de modèle numérique de terrain (MNT) et l'extraction tri-dimensionnelle (3D) d'éléments cartographiques est présenté. Des résultats de ces recherches, on évalue le potentiel de RADARSAT-2 et de trois de ces nouvelles caractéristiques pour les applications cartographiques : le mode ultra-fin, la meilleure connaissance de l'orbite et la polarimétrie. La stéréoscopie et la polarimétrie peuvent être utilisées pour améliorer la création de MNT par rapport à RADARSAT-1. Dans le meilleur des cas, une précision de 5 m (niveau de confiance de 68%) peut être obtenue sur des reliefs modérés. L'extraction 3D d'éléments à partir de données stéréoscopiques du mode ultra-fin peut permettre de respecter les normes de la Base nationale de données topographiques (précision de positionnement meilleure que 10 m, niveau de confiance de 90%). La polarimétrie utilisant deux images d'orbites croisées (quasi orthogonales dans le Nord) peut aussi être utilisée pour créer des MNTs. L'inconvénient majeur est les modèles complexes de rétrodiffusion des données RSO en bande C dans les forêts et les champs agricoles. A court terme, la méthode peut être appliquée sur des sols nus. Toutes ces améliorations prévues devront être vérifiées avec des données réelles.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".